Use Dask when you need to work with datasets that are too large to fit into memory on a single machine. It allows you to perform data manipulation and analysis on those large datasets efficiently. For example, if you have a log file that is several gigabytes in size and you want to process it for analysis, Dask can load and process this data in parallel, allowing you to work with it without running into memory issues.
Pandas is great for small to medium-sized datasets that fit into memory. It has a rich set of features and is simpler to use for straightforward data processing tasks. Use Pandas when you need fast, efficient data manipulation and the dataset is small enough to handle in memory. For example, if you're analyzing a CSV file with a few hundred thousand rows, Pandas would be a better choice due to its simplicity and performance.